The present invention relates generally to ultrasonic imaging, and more particularly to a technique for increasing a signal-to-noise ratio and reducing speckle in ultrasonic images used for making medical diagnoses.
Ultrasonic imaging, which is used to provide visual representations of tissues in patients so medical personnel may make appropriate diagnoses, is performed using apparatus including a transducer having elements that emit ultrasonic energy into the body of a patient. The energy is reflected by tissue in the body and the reflected energy is converted to an electrical signal by other elements in the transducer. The intensity of the electrical signal varies with the characteristics of the tissue. The elements in the transducer are typically arranged in an array and the output from the elements is displayed as an image on a video monitor.
The usefulness of ultrasonic imaging is somewhat limited by a low signal-to-noise ratio in the resulting images. When ultrasonic energy is reflected by a specular target such as a tissue interface having relatively large and generally planar surfaces, the reflected energy provides a distinct image. However, energy reflected from different depths in the body or from curved surfaces may be out of phase with other reflected energy. As a result, the energy may either subtract from or add to other reflected energy, causing holes and bright spots in the image. When ultrasonic energy is reflected from small discrete targets such as cell structures within the tissue having dimensions on the order of the wavelength of the ultrasonic energy, the reflected energy scatters in all directions causing spherical wave fronts. For this reason, these small discrete targets are referred to as “scatterers”. The spherical wave fronts subtract from and add to each other, producing a finely textured salt-and-pepper interference pattern superimposed on the image produced by specular targets. This pattern is commonly referred to as acoustic speckle and may have an intensity equal to or greater than other features of the image. Acoustic speckle blurs the edges of images produced by specular targets and degrades the resolution of the resulting image. Further, the speckle obscures information about the small targets.
Most previous attempts to reduce speckle in ultrasonic images use averaging techniques that reduce speckle by reducing small scale variations in the image. Reducing small scale variations blurs the image. Although blurring the image can be useful because it reduces pseudo-random variability such as speckle, it can also significantly reduce image quality by obscuring boundaries and small scale features.
Other attempts to reduce speckle in ultrasonic images have used higher-order statistics. One method discriminates different tissue textures by assuming a single, well-defined spatial texture scale. Linear and higher order statistical terms are added, and an estimated noise curve is subtracted from the signal to locate features within a feature space. This approach assumes the return can be represented as “signal plus noise”. Small-scale details are treated as noise and subtracted from the ultrasound signal. Thus, this approach is similar to an averaging approach. Moreover, subtraction frequently magnifies errors when the signal includes a large amount of noise, which is not uncommon where the signal is highly attenuated.
Another family of approaches for reducing speckle involves comparing images taken under slightly different conditions and assumes high speckle regions have a greater relative difference than low speckle regions. For example, one method uses a pair of images in which the transducer is moved slightly between obtaining data for the first image and obtaining data for the second image. Subtracting the data obtained for the second image from that obtained from the first image, shows regions of high variability such as resulting from speckle. However, since speckle is random, this method does not detect all speckle. Further, high variability also results from small features and boundaries that may be important in diagnoses, but this method obscures these features. Moreover, the subtraction technique used in this method sometimes magnifies errors.
Still other approaches use asymmetric gradient operators. The use of gradient operators also involves subtraction and has the inherent problems associated with subtraction such as loss of small scale information and potential magnification of errors. Further, since boundaries also produce large gradients, important features can be missed because this method regards them as noise.